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Diffstat (limited to 'test/test_sample_cdf.py')
-rw-r--r-- | test/test_sample_cdf.py | 67 |
1 files changed, 67 insertions, 0 deletions
diff --git a/test/test_sample_cdf.py b/test/test_sample_cdf.py new file mode 100644 index 0000000..1510768 --- /dev/null +++ b/test/test_sample_cdf.py @@ -0,0 +1,67 @@ +import pycuda.driver as cuda +from pycuda.compiler import SourceModule +from pycuda import gpuarray +import numpy as np +import ROOT +import os +import unittest +import chroma + +class TestSampling(unittest.TestCase): + def setUp(self): + self.context = chroma.gpu.create_cuda_context() + current_directory = os.path.split(os.path.realpath(__file__))[0] + from chroma.cuda import srcdir as source_directory + source = open(current_directory + '/test_sample_cdf.cu').read() + self.mod = SourceModule(source, options=['-I' + source_directory], no_extern_c=True, cache_dir=False) + self.test_sample_cdf = self.mod.get_function('test_sample_cdf') + + def compare_sampling(self, hist, reps=10): + nbins = hist.GetNbinsX(); + xaxis = hist.GetXaxis() + intg = hist.GetIntegral() + cdf_y = np.empty(nbins+1, dtype=float) + cdf_x = np.empty_like(cdf_y) + + cdf_x[0] = xaxis.GetBinLowEdge(1) + cdf_y[0] = 0.0 + for i in xrange(1,len(cdf_x)): + cdf_y[i] = intg[i] + cdf_x[i] = xaxis.GetBinUpEdge(i) + + cdf_x_gpu = gpuarray.to_gpu(cdf_x.astype(np.float32)) + cdf_y_gpu = gpuarray.to_gpu(cdf_y.astype(np.float32)) + block =(128,1,1) + grid = (128, 1) + out_gpu = gpuarray.empty(shape=int(block[0]*grid[0]), dtype=np.float32) + + out_h = ROOT.TH1D('out_h', '', hist.GetNbinsX(), + xaxis.GetXmin(), + xaxis.GetXmax()) + out_h.SetLineColor(ROOT.kGreen) + + for i in xrange(reps): + self.test_sample_cdf(np.int32(i), + np.int32(len(cdf_x_gpu)), + cdf_x_gpu, cdf_y_gpu, out_gpu, block=block, grid=grid) + out = out_gpu.get() + for v in out: + out_h.Fill(v) + + prob = out_h.KolmogorovTest(hist) + return prob, out_h + + def test_sampling(self): + '''Verify that the CDF-based sampler on the GPU reproduces a binned + Gaussian distribution''' + f = ROOT.TF1('f_gaussian', 'gaus(0)', -5, 5) + f.SetParameters(1.0/np.sqrt(np.pi * 2), 0.0, 1.0) + gaussian = ROOT.TH1D('gaussian', '', 100, -5, 5) + gaussian.Add(f) + + prob, out_h = self.compare_sampling(gaussian, reps=50) + + assert prob > 0.01 + + def tearDown(self): + self.context.pop() |